In general the initial configuration of industrial and other types of systems assumes the system is new, but as conditions change that early configuration may no longer be optimal. Equipment performance can have severe impacts when operation of the equipment is not associated with prescribed goals. For example, in a production line a certain machine can be targeted to perform different behaviors over time, such as targeting autonomy, energy consumption, or minimize maintenance. There is a need to optimize the use of equipment according to the operator's changing needs, as well as to configure user presets or initial setup to target the desired optimization choice.
For example, predictive maintenance is one area in which equipment is frequently monitored in key variables produced by specific sensors that can sense power consumption, vibration, temperature, humidity, and the like. Prior art FIG. 1A illustrates this concept for evaluating a single bearing for lubrication; in an early phase the bearing is new but as it continues in use over time different sensors can evaluate different aspects of the bearing's performance that may indicate a lubrication issue. Some sensors may look at the bearing itself while others look at upstream components such as the oil supply line at prior art FIG. 1B. Eventually with no corrective action the bearing fails, which the final sensor P6 senses as excessive heat. By executing an analysis over these combined monitored data, it is possible to predict which kind of defect the machine would present. In this case, there is an opportunity to dynamically change the machine's behavior in order to minimize such possible failures or to meet another goals in real-time, for example by managing an electronic equipment controller to slow down the speed at which the equipment is operating, thus extending the period necessary to execute the maintenance.
Some relevant teachings in this regard can be seen at the following patent document references.                US 20140047107 A1—Remote industrial monitoring and analytics using a cloud infrastructure;        US 20140047064 A1—Remote industrial monitoring using a cloud infrastructure;        WO 2010120442 A2—Cloud computing as a basis for equipment health monitoring service;        WO 2011106914 A1—Device monitoring system and method based on cloud computing;        US 20130212214 A1—Cloud gateway for industrial automation information and control systems; and        EP 2414956 A2—Cloud computing for an industrial automation and manufacturing system        
Further relevant teachings in this regard can be seen at the following non-patent documents.                A review on machinery diagnostics and prognostics implementing condition-based maintenance [Mechanical Systems and Signal Processing 20; Andres K. S. Jardine, Daming Lin, & Dragan Banjevic; Elsevior; 2006; pp. 1483-1510];        Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches [Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches; Gungor, V. C. and Hancke, G. P.; Industrial Electronics, IEEE Transactions on (Volume: 56, Issue: 10); 27 Feb. 2009, pp 4258-4265];        Wireless Sensor Networks for Industrial Environments, by Kay Soon Low, W. N. N. Win, and Meng Joo Er; [Computational Intelligence for Modeling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on (Volume: 2); 28-30 Nov. 2006, Vienna, Austria; pp 271-276];        Wireless sensor networks for industrial applications, by Xingfa Shen, Zhi Wang and Youxian Sun; [Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on (Volume: 4); 15-19 Jun. 2004; pp 3636-3640];        Wireless sensor network for industrial automation, by M. Yamaji, Y. Ishi, T. Shimamura and S. Yamamota; [Networked Sensing Systems, 2008. INSS 2008. 5th International Conference on; 17-19 Jun. 2008, p 253];        Industrial Control using Wireless Sensor Networks, by K. Khakpour and M. H. Shenassa; [Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on; 7-11 Apr. 2008, Damascus, Syria]; and        Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications [Mechanical Designs and Signal Processing 42; Jay Lee, Fangji Wu, Wenyu Zhao, Masoud Ghaffri, Linxia Liao & David Siegel; Elsevior; 2014; pp. 313-334].        